Aerial Dispersal and Multiple-Scale Spread of Epidemic Disease

Mundt, Christopher C.; Sackett, Kathryn E.; Wallace, LaRae D.; Cowger, Christina; Dudley, Joseph P.

doi:10.1007/s10393-009-0251-z

Cited by 45 publications

(51 citation statements)

References 27 publications

(32 reference statements)

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“…Recovery of the released clone followed a logistic regression model, and was significant {P < 0.041 for all slope term scenarios) as a function of distance from the source of inoculum. Logistic regression models are commonly used in epidemiology to model dispersal of pathogens and disease progression (6,15,26). The slope term (ßi) in our regression analysis was always negative, indicafing a decrease in the recovery of the released clone as the distance from the source of inoculum increased.…”

Section: Discussionmentioning

confidence: 99%

Monitoring the Long-Distance Transport of Fusarium graminearum from Field-Scale Sources of Inoculum

Prussin

Malla

et al. 2014

Plant Disease

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Prussin, A. J., II, Li, Q., Malla, R., Ross, S. D., and Schmale, D. G., III. 2014. Monitoring the long-distance transport of Fusarium graminearum from field-scale sources of inoculum. Plant Dis. 98:504-511.The fungus Fusarium graminearum causes Fusarium head blight (FHB) of wheat. Little is known about dispersal of the fungus from field-scale sources of inoculum. We monitored the movement of a clonal isolate off: graminearum from a 3,716 m-(0.372 ha) source of inoculum over two field seasons. Ground-based collection devices were placed at distances of 0 (in the source), 100, 250, 500, 750, and 1,000 m from the center of the clonal sources of inoculum. Three polymorphic microsatellites were used to identify the released clone from 1,027 isolates (790 in 2011 and 237 in 2012) of the fungus. Results demonstrated that the recovery of the released clone decreased at greater distances from the source. The majority (87%, 152/175 in 2011; 77%, 74/96 in 2012) of the released clone was recaptured during the night (1900 to 0700). The released clone was recovered up to 750 m from the source. Recovery of the released clone followed a logisfic regression model and was significant (P < 0.041 for all slope term scenarios) as a function of distance from the source of inoculum. This work offers a means to experimentally determine the dispersal kemel of a plant pathogen, and could be integrated into management strategies for FHB.Many plant pathogens use the atmosphere to move over long distances (1,2), including Peronospora tabacina, causal agent of tobacco blue mold (4), Phakopsora pachyrhizi, causal agent of Asian soybean mst (4,21), and Puccinia graminis f sp. tritici, causal agent of wheat stem rust (40). The atmospheric transport of plant pathogens is broadly characterized by the aerobiological processes of hberation, horizontal transport, and deposifion (18). A detailed understanding of these processes is a prerequisite to the development of successful plant disease management strategies.Another important plant pathogen that utilizes the atmosphere for long-distance transport is Fusarium graminearum (synonym Gibberella zeae) (24,37). F. graminearum is a fungal plant pathogen that causes Fusarium head blight (FHB) of wheat and barley, which has resulted in more than $3 billion in crop losses in the United States over the past two decades (25,29,33). The fungus produces a mycotoxin known as deoxynivalenol (DON) that may contaminate food and feed and threaten the health of humans and livestock (38,41).The disease cycle of F. graminearum has been well studied (33). The fungus overwinters in crop debris (e.g., residues of wheat, com, and barley) from previous growing seasons (12). In the spring, perithecia develop on crop residues and forcibly discharge ascospores millimeter-scale distances (11,32,42,43). These ascospores have the potential to move over long distances by wind (24,36), and may infect fields in neighboring regions. Dill-Macky and Jones (10) found that FHB incidence and severity were highest when wheat followed a sus...

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Section: Discussionmentioning

confidence: 99%

Monitoring the Long-Distance Transport of Fusarium graminearum from Field-Scale Sources of Inoculum

Prussin

Malla

et al. 2014

Plant Disease

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“…But what can disease-focused studies offer the science of biogeography beyond additional case studies? The article by Mundt et al (2009) provides a striking example of how the study of the spread of pathogens can provide strong evidence of a general law of spread, one that is potentially applicable to a wide range of organisms.…”

Section: Rates Of Spreadmentioning

confidence: 98%

The Intersection of the Sciences of Biogeography and Infectious Disease Ecology

Scheiner

2009

EcoHealth

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Our understanding of disease ecology can be enhanced by the use of spatially explicit models and a biogeographic perspective. For example, disease emergence is partially a function of shifts in the geographic ranges of pathogens and hosts. Biogeographic approaches can be used to help us understand the interaction of host and pathogen diversity. Climate change is a global phenomenon that will require a biogeographic perspective if we are to predict its affect on global disease burden. Studies of disease ecology can enhance our general understanding of the effects of global change on species' distributions by providing useful case studies for developing and testing models. Possibly most important, studies of the spread of pathogens has the promise of giving us a general law of species' spread. Currently, biogeography and disease ecology are disciplinary communities with little overlap. The articles in this special feature are designed to help bridge that gap.

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“…Thus, a scaled version of Eq. (5) that is useful to compare different epidemics, can be rewritten as (Ferrandino, 1993; Madden, Hughes & Van den Bosch, 2007; Mundt et al, 2009a; Mundt et al, 2009b): In Eq. (6), ν t for different epidemics will fall on a line with a slope of b irrespective of r but a faster epidemic will have a greater s 0 and would reach any distance much faster than a slower epidemic (Mundt et al, 2009a; Mundt et al, 2009b).…”

Section: Introductionmentioning

confidence: 99%

“…Analytically, Eq. (6) can be used to estimate the value of b using a spatial regression approach (Madden, Hughes & Van den Bosch, 2007; Mundt et al, 2009a; Mundt et al, 2009b). …”

Section: Introductionmentioning

confidence: 99%

“…Over time, s t becomes exponentially more distant from s 0 (Madden, Hughes & Van den Bosch, 2007) and using one unit time steps, the position of the wave-front can be described as (Mundt et al, 2009a; Mundt et al, 2009b): Equation (7) can be linearized as: The parameter b can subsequently be estimated using the temporal regression approach from Eq. (8) based on the slope of the regression of ln( s t ) on t .…”

Section: Introductionmentioning

confidence: 99%

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Focus expansion and stability of the spread parameter estimate of the power law model for dispersal gradients

Ojiambo

Gent

Mehra

et al. 2017

PeerJ

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Empirical and mechanistic modeling indicate that pathogens transmitted via aerially dispersed inoculum follow a power law, resulting in dispersive epidemic waves. The spread parameter (b) of the power law model, which is an indicator of the distance of the epidemic wave front from an initial focus per unit time, has been found to be approximately 2 for several animal and plant diseases over a wide range of spatial scales under conditions favorable for disease spread. Although disease spread and epidemic expansion can be influenced by several factors, the stability of the parameter b over multiple epidemic years has not been determined. Additionally, the size of the initial epidemic area is expected to be strongly related to the final epidemic extent for epidemics, but the stability of this relationship is also not well established. Here, empirical data of cucurbit downy mildew epidemics collected from 2008 to 2014 were analyzed using a spatio-temporal model of disease spread that incorporates logistic growth in time with a power law function for dispersal. Final epidemic extent ranged from 4.16 ×108 km2 in 2012 to 6.44 ×108 km2 in 2009. Current epidemic extent became significantly associated (P < 0.0332; 0.56 < R2 < 0.99) with final epidemic area beginning near the end of April, with the association increasing monotonically to 1.0 by the end of the epidemic season in July. The position of the epidemic wave-front became exponentially more distant with time, and epidemic velocity increased linearly with distance. Slopes from the temporal and spatial regression models varied with about a 2.5-fold range across epidemic years. Estimates of b varied substantially ranging from 1.51 to 4.16 across epidemic years. We observed a significant b ×time (or distance) interaction (P < 0.05) for epidemic years where data were well described by the power law model. These results suggest that the spread parameter b may not be stable over multiple epidemic years. However, b ≈ 2 may be considered the lower limit of the distance traveled by epidemic wave-fronts for aerially transmitted pathogens that follow a power law dispersal function.

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Aerial Dispersal and Multiple-Scale Spread of Epidemic Disease

Cited by 45 publications

References 27 publications

Monitoring the Long-Distance Transport of Fusarium graminearum from Field-Scale Sources of Inoculum

Monitoring the Long-Distance Transport of Fusarium graminearum from Field-Scale Sources of Inoculum

The Intersection of the Sciences of Biogeography and Infectious Disease Ecology

Focus expansion and stability of the spread parameter estimate of the power law model for dispersal gradients

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